There are numerous reasons for classifying entities. Binary classification indicates whether or not an entity is in a particular class. Classification can be done based on the publications of an entity. This can include social media publications. The social media publications are analyzed for the presence of indicators. The indicators might be key words. The presence or absence of an indicator might be digitally stored as a binary value of 1 if said indicator is present and a binary value of 0 if said indicator is not present. Prior art systems have assigned different weights to different indicators. This recognizes that some indicators are stronger than others. It has been discovered, however, that when there is a large number of low weight indicators in an entity's publications, prior art systems tend to over predict the probability that an entity is in a particular class. There is need, therefore, for an artificial intelligence system for training a classifier that will not over predict due to large numbers of low weight indicators.